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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Inflammation
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1539465
This article is part of the Research Topic Big Data and Precision Medicine: Diagnosis and Treatment, Drug Discovery, and Integration of Multiple Omics View all 9 articles

Developing and Validating a Machine Learning Model to Predict Multidrug-Resistant Klebsiella pneumoniae-Related Septic Shock

Provisionally accepted
Shengnan Pan Shengnan Pan 1Ting Shi Ting Shi 2Jinling Ji Jinling Ji 1Kai Wang Kai Wang 3Kun Jiang Kun Jiang 1Yabin Yu Yabin Yu 2*Chang LI Chang LI 1*
  • 1 Department of Medical laboratory, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China, Huaian, China
  • 2 Department of Hepatobiliary and Pancreatic Surgery, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China, Huaian, China
  • 3 Department of Rheumatology, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China, Huaian, China

The final, formatted version of the article will be published soon.

    Background: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions. Methods: A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its optimal balance between performance and robustness against overfitting. Results: The overall incidence of MDR-KP-associated septic shock was 16.32% (226/1,385). The predictive model identified seven key risk factors: procalcitonin (PCT), sepsis, acute kidney injury, intra-abdominal infection, use of vasoactive medications, ventilator weaning failure, and mechanical ventilation. The logistic regression model demonstrated excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.906 in the training set and 0.865 in the validation set. Calibration was robust, with Hosmer-Lemeshow test results of P = 0.065 (training) and P = 0.069 (validation). Decision curve analysis indicated substantial clinical net benefit. Conclusion: This study presents a validated, high-performing predictive model for MDR-KP-associated septic shock, offering a valuable tool for early clinical decision-making. Prospective, multi-center studies are recommended to further evaluate its clinical applicability and effectiveness in diverse settings.

    Keywords: multidrug-resistant Klebsiella pneumoniae, septic shock, machine learning, predictive model, Risk factors

    Received: 04 Dec 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Pan, Shi, Ji, Wang, Jiang, Yu and LI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yabin Yu, Department of Hepatobiliary and Pancreatic Surgery, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China, Huaian, China
    Chang LI, Department of Medical laboratory, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu, China, Huaian, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.